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Record W1978913368 · doi:10.1002/jrs.1738

Adiabatic climbing of vibrational ladders using Raman transitions with chirped pump lasers: effect of higher electronic surfaces and control of the shapes of vibrational wave packets

2007· article· en· W1978913368 on OpenAlexaff
Deyana S. Tchitchekova, Szczepan Chelkowski, André D. Bandrauk

Bibliographic record

VenueJournal of Raman Spectroscopy · 2007
Typearticle
Languageen
FieldPhysics and Astronomy
TopicLaser-Matter Interactions and Applications
Canadian institutionsUniversité de Sherbrooke
Fundersnot available
KeywordsWave packetAdiabatic processExcitationStimulated Raman adiabatic passageAtomic physicsPhotonLaserPopulationRaman spectroscopyPhysicsMolecular physicsOpticsQuantum mechanics

Abstract

fetched live from OpenAlex

Abstract Using numerical solutions of the time‐dependent Schrödinger equation for the H 2 molecule in intense laser fields we calculate the vibrational excitation induced by the Raman chirped adiabatic process (RCAP). We show that adding several higher electronic surfaces to a simple two‐surface model improves the efficiency of the ladder‐climbing process at intensities below the adiabaticity threshold. Furthermore, we show that although using photon energies close to the one‐photon electronic transition frequency allows the use of lower pump and Stokes intensities, in general, this leads to more population transfer to the upper electronic surfaces accompanied by a loss of selectivity in the vibrational excitation on the ground‐state surface. By contrast, considerable dissociation yields can be achieved when higher energy photons are used. We also investigate the structure of time‐dependant vibrational wave packets prepared by RCAP. We find that at specific times the wave packet is very well localised at large inter‐nuclear separations at which ionisation occurs with a probability 3 orders of magnitude larger than at the equilibrium separation. Copyright © 2007 John Wiley & Sons, Ltd.

Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.

How this classification was reachedexpand

Full frame distilled prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Bench or experimental · Consensus signal: Bench or experimental
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.066
Threshold uncertainty score0.381

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0000.000

Machine scores (provisional)

The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.

Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.

Opus teacher head0.006
GPT teacher head0.253
Teacher spread0.247 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one teacher head, not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designBench or experimental
Domainnot available
GenreEmpirical

How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".

Quick stats

Citations10
Published2007
Admission routes1
Has abstractyes

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